Survey of Parallel Data Processing in Context with MapReduce

Vaidya, Madhavi

doi:10.5121/csit.2011.1307

Cited by 3 publications

(2 citation statements)

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“…ParallAX is introduced as a new user-friendly tool for visualization and analysis of multivariate data using the parallel coordinates methodology [13]. Other possibilities of visualization via parallel coordinates are discussed in [14][15][16]. The increasing popularity of the parallel coordinates may be illustrated by the number of publications with the term "parallel coordinates" present in their title: It has been rising steadily from 14 in the year 1991 to approximately 543 in 2011, with a total of 5620 publications as reported by Google…”

Section: Minimum Distance Between the Two Lines In Space-timementioning

confidence: 99%

Using of Parallel Coordinates in Finding Minimum Distance in Time-Space

Blažeková¹,

Vojtekova²

2019

Komunikácie

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Airspace domain may be represented by a time-space consisting of a three-dimensional Cartesian coordinate system and time as the fourth dimension. A coordinate system provides a scheme for locating points given its coordinates and vice versa. The choice of coordinate system is important, as it transforms data to geometric representation. Visualization of the three and more dimensional data on the two-dimensional drawing - computer monitor is usually done by projection, which often can restrict the amount of information presented at a time. Using the parallel coordinate system is one of possibilities to present multidimensional data. The aim of this article is to describe basics of parallel coordinate system and to investigate lines and their characteristics in time-space.

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Section: Minimum Distance Between the Two Lines In Space-timementioning

confidence: 99%

Using of Parallel Coordinates in Finding Minimum Distance in Time-Space

Blažeková¹,

Vojtekova²

2019

Komunikácie

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“…Hence, each reducer executes the reduce function which merges all the values in order to generate a smaller set of values. Finally, the final output is generated by grouping all reducer outputs and delivered to a certain location, defined by the job descriptor (Vaidya, 2011).…”

Section: Mapreduce Architecturementioning

confidence: 99%

A Survey on MapReduce Implementations

Al-Badarneh

Mohammad

Harb

2016

International Journal of Cloud Applications and Computing

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A distinguished successful platform for parallel data processing MapReduce is attracting a significant momentum from both academia and industry as the volume of data to capture, transform, and analyse grows rapidly. Although MapReduce is used in many applications to analyse large scale data sets, there is still a lot of debate among scientists and researchers on its efficiency, performance, and usability to support more classes of applications. This survey presents a comprehensive review of various implementations of MapReduce framework. Initially the authors give an overview of MapReduce programming model. They then present a broad description of various technical aspects of the most successful implementations of MapReduce framework reported in the literature and discuss their main strengths and weaknesses. Finally, the authors conclude by introducing a comparison between MapReduce implementations and discuss open issues and challenges on enhancing MapReduce.

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MobisenseCar: A Mobile Crowd-Based Architecture for Data Acquisition and Processing in Vehicle-Based Sensing

Nkenyereye¹,

Jang²

2017

ijacsa

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Abstract-The use of wireless technology via smartphone allows designing smartphone applications based on OBD-II for increasing environment sensing. However, uploading of vehicle's diagnostics data via car driver's tethered smart phone attests a long Internet latency when a large number of concurrent users use the remote mobile crowdsensing server application simultaneously, which increases the communication cost. The large volume of data would also challenge the traditional data processing framework. This paper studies design functionalities of mobile crowdsensing architecture applied to vehicle-based sensing for handling a huge amount of sensor data collected by those vehicle-based sensors equipped with a smart device connected to the OBD-II interface. The proposed MobiSenseCar uses Node.js, a web server architecture based on single-thread event loop approach and Apache Hive platform responsible for analyzing vehicle's engine data. The Node.js is 40% faster than the traditional web server side features thread-based approach. Experiment results show that MapReduce algorithm is highly scalable and optimized for distributed computing. With this mobile crowdsensing architecture it was possible to monitor information of car's diagnostic system condition in real time, improving driving ability and protect the environment by reducing vehicle emissions.

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Survey of Parallel Data Processing in Context with MapReduce

Abstract: MapReduce is a parallel programming model and an associated implementation introduced by

Cited by 3 publications

References 1 publication

Using of Parallel Coordinates in Finding Minimum Distance in Time-Space

Using of Parallel Coordinates in Finding Minimum Distance in Time-Space

A Survey on MapReduce Implementations

MobisenseCar: A Mobile Crowd-Based Architecture for Data Acquisition and Processing in Vehicle-Based Sensing

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